Our architectural choices, such as multi-query attention, enabled us to control speed and latency more precisely without hurting user engagement.

V3 employs multi-head latent attention (MLA) to address key-value storage limitations, compressing matrices into a latent representation and reconstructing them only when necessary, which reduced cache size by 93.3% and increased generation throughput by 5.76 times.

We faced unique challenges in scaling our platform, particularly with GPU local attention caching, which led to unexpected negative results during testing.

Without GPU caching, we would face significant financial challenges, as our 95% cache hit ratio is essential for cost-effective service delivery.

Serving 30,000 messages per second incurs significant costs in GPU spending, and managing latency is crucial for consumer satisfaction.

We cache results at each layer of the model for every generated message, which allows us to handle user messages more efficiently and maintain performance.

Achieving this reduction in inference costs requires solving a complex three-dimensional optimization problem involving quality, speed, and cost.

AI advancements are increasing GPU requirements, as the computational needs for inference are growing significantly.

The total response time has improved from 25 seconds to 12.5 seconds, significantly enhancing user experience by reducing the time to generate responses.